Method for predicting stability of overlying slope of coal mining subsidence area in loess area and related equipment

Abstract

The invention provides a loess area coal mining subsidence area overlying slope stability prediction and space identification method and related equipment. The prediction method comprises the following steps: determining a first hidden danger region in a target region according to environmental factors, structural factors and region adjustment coefficients of a loess region slope; the first hidden danger area comprises at least one coal mining area adopting underground mining and a coal mining subsidence area of the coal mining area; determining a second hidden danger area in the first hidden danger area according to the deformation influence range of the goaf, the mining depth and thickness ratio of the mining coal seam, the coal mining process and the slope type; obtaining stability conditions, influence factors and quantized values of the slopes of the multiple coal mining subsidence areas, inputting the quantized values of the influence factors to train a random forest model, and verifying to obtain a subsidence area slope stability prediction model; and inputting the quantitative value of the factor influencing the stability into the slope stability prediction model to obtain a prediction result of the slope with the instability hidden danger in the second hidden danger area.

Description

Technical Field

[0001] The present application relates to the technical field of slope stability evaluation in coal mining subsidence areas in loess regions, and in particular to a method and related equipment for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions. Background Art

[0002] The ground subsidence, ground fissures, and ground collapse caused by coal mining in the loess region have a certain impact on the stability of the slopes in the upper loess region. Due to the complex terrain conditions and poor engineering geological conditions in the loess region, mining in this special terrain is very likely to cause mining subsidence, which may cause lateral sliding of the slopes in the loess region, local collapse and layer volume changes, water loss consolidation, water-wetting and other additional deformations, and even induce derivative geological disasters such as landslides. Under a certain geological environment background, mining subsidence will also cause serious damage to surface vegetation and land resources, further aggravate soil erosion and land resource degradation, and have an irreversible impact on water resources, land, and natural ecological environment around the mining area. The problems of unstable slopes, soil erosion, and land resource degradation in the loess region caused by mining activities not only threaten the safety of personnel and equipment of mining enterprises, but also bring safety hazards to surrounding residential houses and transportation facilities. Therefore, studying the problems caused by mining activities in the coal mining subsidence area in the loess region has important scientific significance and application value for mining safety and disaster prevention and mitigation in the fields of geotechnical engineering and mining environment. Among them, the slope stability problem is an important research content of geotechnical engineering. The correctness of the slope stability evaluation results in the coal mining subsidence area is directly related to the possibility of preventing and controlling coal mining subsidence-induced geological problems such as slope deformation and instability in the loess area, and achieving timely and effective prevention and repair. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a method and related equipment for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions.

[0004] Based on the above purpose, the present application provides a method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions, including:

[0005] The first hidden danger area in the target area is determined according to the environmental factors of the slopes in the loess region, the structural factors of the slopes in the loess region and the regional adjustment coefficient of the slopes in the loess region; the environmental factors include geographical factors, geological environmental background factors and meteorological and hydrological conditions; the target area is the loess region, including a plurality of coal mining areas and coal mining subsidence areas thereof using underground mining, and has a preset area range; the first hidden danger area includes at least one coal mining area and coal mining subsidence area thereof using underground mining;

[0006] Determine the second hidden danger area in the first hidden danger area according to the location factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining process and the slope type; the second hidden danger area includes at least one slope with instability hidden danger in the coal mining subsidence area; the location factor includes the range of the mined goaf area and the range of the unmined goaf area;

[0007] Obtain quantitative values ​​of stability influencing factors of the slope with instability hazards in the at least one coal mining subsidence area, input them into the random forest slope stability prediction model, and obtain the stability prediction result of the slope in the at least one coal mining subsidence area; the stability prediction result includes stable or unstable; the quantitative values ​​of the stability influencing factors include slope cohesion, slope internal friction angle, slope height, slope angle, average bulk density of slope rock and soil materials, and slope pore pressure ratio.

[0008] In some embodiments, it also includes:

[0009] Different prevention and control measures are taken for the first hidden danger area, the second hidden danger area and the slopes in the coal mining subsidence area with unstable stability prediction results; among which, the slopes in the first hidden danger area are periodically monitored and the slopes in the second hidden danger area are monitored in multiple dimensions; the slopes in the coal mining subsidence area with unstable stability prediction results are treated.

[0010] In some embodiments, the periodic monitoring of the first hidden danger area includes: performing quarterly detection on the slope of the first hidden danger area, and in response to determining that the impact of the first hidden danger area increases between quarters, generating regional warning information of the increased impact of the first hidden danger area between quarters; the warning information is used to indicate that the impact of the first hidden danger area is deepening;

[0011] The multi-dimensional monitoring of the second hidden danger area includes: monthly detection of the slope of the second hidden danger area, and deployment of slope deformation measurement devices and crack sensors for real-time detection, and generating alarm information in response to determining that the slope displacement data and the associated ground crack displacement data both exceed corresponding warning thresholds; the alarm information is used to indicate that there is a risk of deformation and instability in the slope, and an on-site survey is required;

[0012] The treatment of the slope in the coal mining subsidence area whose stability prediction result is unstable includes: for the slope in the coal mining subsidence area whose stability prediction result is unstable, a corresponding treatment strategy is generated according to the geological environment background of the slope and the properties of the slope rock and soil.

[0013] In some of the embodiments, the geographical factors include topographic factors; the geological environment background factors include ground fissure factors, fault factors and earthquake factors; the meteorological and hydrological conditions factors include rainfall factors; and the structural factors include slope height factors, slope gradient factors and slope aspect factors.

[0014] In some embodiments, determining the first hidden danger area in the target area according to the environmental factors of the loess region slope, the structural factors of the loess region slope and the regional adjustment coefficient of the loess region slope comprises:

[0015] Obtaining the value of the geographical factor, adjusting the value of the geographical factor according to the regional adjustment coefficient, and judging the influence of the geographical factor on the slope stability in the loess region according to the adjusted value;

[0016] Obtaining the value of each factor in the geological environment background factor, adjusting the value of each factor in the geological environment background factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of each factor in the geological environment background factor on the stability of the slope in the loess region according to the adjusted value;

[0017] Obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the meteorological and hydrological condition factor on the stability of the slope in the loess region according to the adjusted value;

[0018] Obtaining the values ​​of each factor in the structural factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the slope height and slope gradient data in the structural factor on the slope stability, and the influence of the slope aspect data in the structural factor on the slope stability in the loess region according to the adjusted value;

[0019] In response to determining that the influence of the geographical factors on the slope stability is the first level of influence or the second level of influence, the influence of each factor in the geological environment background factors on the slope stability is the first level of influence, the influence of the meteorological and hydrological conditions on the slope stability is the first level of influence, the influence of the slope height and slope gradient data in the structural factors on the slope stability is the first level of influence, and the influence of the slope aspect data in the structural factors on the slope stability is the first level of influence; determine that the area where the slope is located is the first hidden danger area.

[0020] In some embodiments, the obtaining of the value of the geographical factor, adjusting the value of the geographical factor according to the regional adjustment coefficient of the slope in the loess region, and judging the degree of influence of the geographical factor on the slope stability according to the adjusted value comprises: obtaining the terrain undulation in the digital elevation model DEM data; adjusting the terrain undulation according to the regional adjustment coefficient of the slope in the loess region, and determining the first degree of influence of the terrain and geomorphic factors on the stability of the slope in the loess region according to the preset interval to which the adjusted terrain undulation belongs; the first degree of influence comprises a first level of influence, a second level of influence, a third level of influence, a fourth level of influence or a fifth level of influence;

[0021] The step of obtaining the value of each factor in the geological environment background factors, adjusting the value of each factor in the geological environment background factors according to the regional adjustment coefficient of the slope in the loess region, and judging the influence degree of each factor in the geological environment background factors on the stability of the slope in the loess region according to the adjusted value comprises: obtaining the associated ground fissures in the satellite remote sensing image data; adjusting the associated ground fissures data according to the regional adjustment coefficient of the slope in the loess region, and determining the second influence degree of the ground fissure factors on the stability of the slope in the loess region according to the preset interval to which the width of the adjusted associated ground fissures belongs; the second influence degree comprises the first level influence degree, the second level influence degree, the third level influence degree or the fourth level influence degree;

[0022] Obtaining the fault position in the digital elevation model DEM data and the satellite remote sensing image data; obtaining the fault dip and fault drop in the satellite remote sensing image data; adjusting the fault dip and fault drop according to the regional adjustment coefficient of the slope in the loess region, and determining the third influence degree of the fault factor on the stability of the slope in the loess region according to the relationship between the adjusted fault dip and fault drop and the threshold; the third influence degree includes the influence degree of the first level;

[0023] Determine the fourth degree of influence of earthquake factors on slope stability in the loess region according to the adjusted minimum earthquake magnitude that can induce slope collapse and sliding during a natural earthquake, and the relationship between the minimum site intensity that can induce slope collapse and sliding during a natural earthquake and the threshold value; the fourth degree of influence includes the first level of influence;

[0024] The step of obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence degree of the meteorological and hydrological condition factor on the stability of the slope in the loess region according to the adjusted value comprises: obtaining a water system model in the digital elevation model DEM data, inputting the rainfall monitoring data of the meteorological station in the loess region, and obtaining the regional average rainfall intensity data; adjusting the rainfall intensity data according to the regional adjustment coefficient of the slope in the loess region, and determining the fifth influence degree of the rainfall factor on the stability of the slope in the loess region according to the relationship between the adjusted rainfall intensity data and the preset intensity data; the fifth influence degree includes the influence degree of the first level;

[0025] The method of obtaining the values ​​of each factor in the structural factors, adjusting the values ​​of the meteorological and hydrological condition factors according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the slope height and slope gradient in the structural factors on the stability of the slope in the loess region according to the adjusted values, and the influence of the slope aspect data in the structural factors on the stability of the slope in the loess region include:

[0026] Obtaining the NDVI value corresponding to the slope aspect data in the digital elevation model DEM data; adjusting the NDVI value according to the regional adjustment coefficient of the slope in the loess region, and determining the sixth degree of influence of the slope aspect on the stability of the slope in the loess region according to whether the adjusted NDVI value is a preset value; the sixth degree of influence includes the first level of influence or the second level of influence;

[0027] Obtain slope height and slope gradient data in digital elevation model (DEM) data; adjust the slope height and slope gradient data according to the regional adjustment coefficient of the slope in the loess region, and determine the seventh influence degree of the slope height on the stability of the slope in the loess region and the eighth influence degree of the slope gradient on the slope stability according to the preset interval to which the adjusted slope height belongs; the seventh influence degree includes the first level influence degree, the second level influence degree, the third level influence degree or the fourth level influence degree; the eighth influence degree includes the first level influence degree, the second level influence degree or the third level influence degree.

[0028] In some embodiments, determining the second hidden danger area in the first hidden danger area according to the position factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining process and the slope type includes:

[0029] According to the location of the mined goaf area adjacent to the slope position in the upper and lower comparison diagram of the mine well, the influence range of the mined goaf area is determined according to L1=cosα1·h1+cotβ1·(H1-h1); where L1 represents the collapse influence range; α1 represents the loose layer movement angle; H1 represents the loose layer thickness; β1 represents the bedrock movement angle; h1 represents the burial depth;

[0030] According to the planned unmined goaf position close to the slope position in the upper and lower comparison map of the mine well, the influence range of the unmined goaf is determined according to L2=cosα2·h2+cotβ2·(H2-h2); where L2 represents the collapse influence range; α2 represents the loose layer movement angle; H2 represents the loose layer thickness; β2 represents the bedrock movement angle; h2 represents the burial depth;

[0031] According to the degree of influence of the mining depth and thickness ratio range in the coal mining subsidence area on the surface deformation, the degree of influence of the slope mining technology on the surface deformation and the corresponding degree of influence of the slope type, the hidden danger areas overlying the mined goaf areas and the unmined goaf areas are judged to obtain the second hidden danger areas.

[0032] In some embodiments, the second hidden danger area is determined based on the influence of the interval of the mining depth and thickness ratio of the coal mining subsidence area on the surface deformation, the influence of the coal mining technology of the slope on the surface deformation and the influence of the slope type, and the hidden danger area overlying the mined goaf area and the unmined goaf area is obtained, and the second hidden danger area includes:

[0033] Obtaining the mining depth and thickness ratio of the coal mining subsidence area, and determining the ninth degree of influence of the interval to which the mining depth and thickness ratio of the coal mining subsidence area belongs on the surface deformation; the ninth degree of influence includes the first level of influence, the second level of influence, the third level of influence, the fourth level of influence or the fifth level of influence;

[0034] Obtaining the coal mining technology of the slope, and determining the tenth degree of influence of the coal mining technology of the slope on the surface deformation; the tenth degree of influence includes the first level of influence or the second level of influence;

[0035] Obtain the type of slope, including rock slope or soil slope; determine the eleventh degree of influence of the type of rock slope on slope stability, including the first degree of influence, the second degree of influence, the third degree of influence or the fourth degree of influence; or obtain the type of foundation soil of the soil slope, determine the twelfth degree of influence of the foundation soil type on slope stability, including the first degree of influence;

[0036] In response to determining that the ninth impact degree is the first level impact degree or the second level impact degree, the tenth impact degree is the first level impact degree, and the eleventh impact degree or the twelfth impact degree is the first level impact degree, the area where the slope is located is determined to be a second hidden danger area.

[0037] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the program.

[0038] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the methods described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A framework diagram of a method for predicting stability of overlying slopes in coal mining subsidence areas in loess regions according to an embodiment of the present application;

[0041] Figure 2 A schematic diagram of a flow chart of a method for predicting stability of an overlying slope in a coal mining subsidence area in a loess region according to an embodiment of the present application;

[0042] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0044] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] Although the proportion of coal in total energy consumption has declined, its dominant position will not change in the future because renewable energy is unlikely to replace traditional fossil energy on a large scale in the short term. Affected by factors such as topography, mining conditions, engineering geology and hydrogeology, the restoration and monitoring technologies for mining environmental problems caused by coal mining subsidence areas are different. At the same time, due to the lack of systematic theoretical and technical guidance in the study of the impact range of coal mining subsidence areas on overlying buildings, the progress of related governance and utilization technologies has been limited. The research on the investigation and evaluation methods of slope stability in coal mining subsidence areas is of great significance. In the monitoring and diagnosis technology of land and environment in mining, the traditional ground monitoring method has been developed to the integrated monitoring of "star-air-ground-well", and technical means such as the use of continuous normalized difference vegetation index (NDVI) time series data to monitor the changes in vegetation status in mining areas and the use of ground penetrating radar for reclamation tracking monitoring have been developed. In the governance technology of coal mining subsidence, the technical methods of non-filling reclamation have been developed from the initial non-filling reclamation method to the filling reclamation method, such as the Yellow River sediment filling reclamation technology. However, there is still a gap in the restoration and monitoring technology of mining environmental problems caused by coal mining subsidence areas, which can detect the slope stability under the influence of mining by spatial superposition of multiple evaluation factors affecting slope stability based on remote sensing imaging technology.

[0046] Based on this, the embodiment of the present application provides a method and related equipment for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions. By performing a three-level prediction of the stability of the slopes in coal mining subsidence areas, the initial judgment is made by mainly considering the external geological conditions and the internal slope structure to identify the areas with hidden dangers; then the factors affecting the ground subsidence in the coal mining subsidence areas are considered to further accurately judge the scope of the hidden danger areas; finally, a detailed judgment of the on-site identification of slopes in high-risk loess areas based on machine learning is made, forming a set of effective methods for coarse-to-fine and graded screening, and different slope management is carried out for each grade, which can solve the problem of inaccurate prediction of the slope stability in coal mining subsidence areas in related technologies to a certain extent.

[0047] like Figure 1 and Figure 2 As shown, the embodiment of the present application provides a method for predicting the stability of the overlying slope in the coal mining subsidence area in the loess region, which may include:

[0048] Step S100, determining a first hidden danger area in a target area according to environmental factors of a loess region slope, structural factors of a loess region slope and a regional adjustment coefficient of a loess region slope; the environmental factors include geographical factors, geological factors and meteorological factors; the target area is a loess region, including a plurality of coal mining areas and coal mining subsidence areas thereof using underground mining, and having a preset regional range; the first hidden danger area includes at least one coal mining area and coal mining subsidence area thereof using underground mining; the regional range of the obtained first hidden danger area is smaller than the regional range of the loess region;

[0049] Step S200, determining a second hidden danger area in the first hidden danger area according to the location factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining process and the slope type; the second hidden danger area includes at least one slope with instability hidden danger in the coal mining subsidence area; the location factor includes the range of the mined goaf area and the range of the unmined goaf area;

[0050] Step S300, obtaining the quantitative values ​​of the stability influencing factors of the slope with instability hazards in at least one coal mining subsidence area in the loess region, and inputting them into the random forest slope stability prediction model to obtain the stability prediction results of the slope with instability hazards in the at least one coal mining subsidence area; the stability prediction results include stable or unstable; the quantitative values ​​of the stability influencing factors include cohesion, internal friction angle of the slope, slope height, slope angle, average bulk density of slope rock and soil materials, and pore pressure ratio.

[0051] The method for predicting the stability of the overlying slopes in coal mining subsidence areas in loess regions provided in the embodiment of the present application can make a good prediction of the stability of the slopes in coal mining subsidence areas through multi-level judgment and comprehensive consideration of multiple factors. It can improve the prediction efficiency of the stability of the slopes in coal mining subsidence areas to a certain extent, and facilitate targeted and effective management in the later stage.

[0052] In some of the embodiments, in step S100, the distribution of loess areas is generally distributed throughout the country, mainly in the arid and semi-arid areas north of the Kunlun Mountains-Qilian Mountains-Qinling Mountains-Ludong Mountain Area and Liaodong Peninsula, including the Heilongjiang-Jilin-Liaoning area, the Shanxi-Hebei-Shandong-Henan area; the Shaanxi-Gansu-Ningxia-Qinghai-Xinjiang area. Usually, there are certain differences in the topography, hydrology, climate, geological structure and other characteristic elements of each area. For the target area, it has a preset regional range, which is determined according to the distribution of the coal mining area. For example, for the case where there are a large number of mining areas, it can be a range of 25 square kilometers. It can be understood that the area to which the target area belongs is not limited in the embodiment of the present application, and for a target area, the area to which it belongs is usually determined and unique. The regional adjustment coefficient is a coefficient for adjusting the environmental factors of the slope of the loess area, the structural factors of the slope of the loess area and the type of loess based on the area to which the target area belongs, so as to balance the differences in the topography, hydrology, climate, geological structure and other characteristic elements of the area to which the target area belongs to a certain extent, to a certain extent improve the adaptability of the overlying slope stability prediction method of the coal mining subsidence area in the loess area of ​​the present application, and then to a certain extent improve the accuracy. For example, the regional adjustment coefficient of Heilongjiang, Jilin and Liaoning regions can be set to 1.3, the regional adjustment coefficient of Shanxi, Hebei, Shandong and Henan regions can be set to 1.5, the regional adjustment coefficient of Shaanxi, Gansu, Ningxia, Qinghai and Xinjiang regions can be set to 1.7, and the regional adjustment coefficients of other regions outside the aforementioned regions can be set to 1.0.

[0053] In some of the embodiments, the environmental factors of the slopes in the loess region can be understood as external factors, which may include geographical factors, geological environmental background factors, and meteorological and hydrological conditions. Among them, the geographical factors include topographic factors; the geological environmental background factors include ground fissure factors, fault factors, and earthquake factors; and the meteorological and hydrological conditions include rainfall factors. The structural factors of the slopes in the loess region may include slope height, slope gradient, and slope aspect. Usually, the relevant data of these factors can be obtained through digital elevation model DEM data and remote sensing images.

[0054] In this way, the first hidden danger area is determined by the environmental factors of the slopes in the loess region, the structural factors of the slopes in the loess region and the regional adjustment coefficient of the slopes in the loess region. It is possible to comprehensively consider the external and internal factors that affect the region and the stability of the slopes under natural conditions, and then identify the slopes with hidden dangers in the determined area (such as the target area) through multi-factor coupling, that is, to identify the hidden danger slopes at the regional level.

[0055] In some embodiments, determining the first hidden danger area in the target area according to the environmental factors of the loess region slope, the structural factors of the loess region slope and the regional adjustment coefficient of the loess region slope may include:

[0056] Obtaining the value of the geographical factor, adjusting the value of the geographical factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the geographical factor on the stability of the slope in the corpus luteum region according to the adjusted value;

[0057] Obtaining the value of each factor in the geological environment background factor, adjusting the value of each factor in the geological environment background factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of each factor in the geological environment background factor on the stability of the slope in the loess region according to the adjusted value;

[0058] Obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the meteorological and hydrological condition on the stability of the slope in the loess region according to the adjusted value;

[0059] Obtaining the values ​​of each factor in the structural factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the slope height and slope degree data in the structural factor on the slope stability and the influence of the slope aspect data in the structural factor on the slope stability according to the adjusted values;

[0060] In response to determining that the degree of influence of the geographical factors on the slope stability is at the first level or the second level, the degree of influence of each factor in the geological environment background factors on the slope stability is at the first level, the degree of influence of the meteorological and hydrological condition factors on the slope stability is at the first level, the degree of influence of the slope height and slope gradient data in the structural factors on the slope stability is at the first level, and the degree of influence of the slope aspect data in the structural factors on the slope stability is at the first level; it is determined that the area where the slope is located is the first potential hazard area.

[0061] Generally, the area range of the first potential hazard area can be smaller than the area range of the loess area, and the first potential hazard area includes at least one coal mining area using underground mining, its coal mining subsidence area, and multiple slopes. By screening the first potential hazard area, the detection efficiency and accuracy of the slope stability in the coal mining subsidence area can be improved.

[0062] In some embodiments, the obtaining the values of the geographical factors, adjusting the values of the geographical factors according to the regional adjustment coefficient of the slopes in the loess area, and judging the degree of influence of each factor in the geographical factors on the slope stability according to the adjusted values may include:

[0063] Step S111, obtaining the terrain undulation degree in the digital elevation model (DEM) data; adjusting the terrain undulation degree according to the regional adjustment coefficient of the slopes in the loess area, and determining the first degree of influence of the terrain and geomorphology factors on the slope stability according to the preset interval to which the adjusted terrain undulation degree belongs; the first degree of influence includes the first level of influence degree, the second level of influence degree, the third level of influence degree, the fourth level of influence degree, or the fifth level of influence degree. Exemplarily, the first level of influence degree may be the greatest influence, the second level of influence degree may be a very large influence, the third level of influence degree may be a relatively large influence, the fourth level of influence degree may be a relatively small influence, and the fifth level of influence degree may be the smallest influence.

[0064] In some embodiments, in step S111, the DEM data and the range of the target area may be loaded, and the terrain undulation degree may be calculated by the general formula R = H max -H min wherein, R is the terrain undulation degree, and H max is the maximum elevation value within the unit area, and H min is the minimum elevation value within the unit area. The DEM <1000 can be assigned 1 by reclassification; 1000 < DEM < 3500 is assigned 2; 3500 < DEM < 5000 is assigned 3; 5000 < DEM is assigned 4. Through focal statistics, the minimum (i.e., H min ) and the maximum value (i.e., H max)。The terrain undulation degree raster can be obtained through reclassification, where 1 is assigned when 0 < R < 30 (for example, the influence degree of the fifth level); 2 is assigned when 30 ≤ R < 200 (for example, the influence degree of the fourth level); 3 is assigned when 200 ≤ R < 500 (for example, the influence degree of the third level); 4 is assigned when 500 ≤ R < 1000 (for example, the influence degree of the second level); 5 is assigned when 1000 < R (for example, the influence degree of the first level). That is, the preset intervals to which the terrain undulation degree belongs can include: 0 < R < 30, 30 ≤ R < 200, 200 ≤ R < 500, 500 ≤ R < 1000, and 1000 < R. Subsequently, the reclassified DEM and the terrain undulation degree raster can be converted to polygons and combined, and the terrain and geomorphic types corresponding to the values of the reclassified DEM and the terrain undulation degree raster are paired one by one to obtain the geomorphic classification and division map of the DEM and the terrain undulation degree. Among them, for the adjusted terrain undulation degree R, the flat terrain with 0 < R < 30 is initially judged to have the least influence on the slope stability; the general terrain with 30 < R < 200 is initially judged to have a relatively small influence on the slope stability; the irregular terrain with 200 < R < 500 is initially judged to have a relatively large influence on the slope stability; the complex terrain with 500 < R < 1000 is initially judged to have a very large influence on the slope stability; the extreme terrain with 1000 < R is initially judged to have the greatest influence on the slope stability.

[0065] In some of the embodiments, obtaining the values of the various factors in the geological environment background factors, adjusting the values of the various factors in the geological environment background factors according to the regional adjustment coefficient of the slopes in the loess area, and judging the influence degree of the various factors in the geological environment background factors on the slope stability in the loess area may include:

[0066] Step S121, obtaining the associated ground fissures formed due to mining activities in the satellite remote sensing image data; adjusting the associated ground fissures according to the regional adjustment coefficient of the slopes in the loess area, and determining the second influence degree of the ground fissure factor on the slope stability according to the preset interval to which the width of the adjusted associated ground fissures belongs; the second influence degree includes the influence degree of the first level, the influence degree of the second level, the influence degree of the third level, or the influence degree of the fourth level. Exemplarily, the influence degree of the first level may be the greatest influence, the influence degree of the second level may be a very large influence, the influence degree of the third level may be a relatively large influence, the influence degree of the fourth level may be a relatively small influence, and the influence degree of the fifth level may be the least influence.

[0067] Step S122, obtaining the fault positions in the digital elevation model (DEM) data and the satellite remote sensing image data; obtaining the fault dip angle and the fault throw in the satellite remote sensing image data; adjusting the fault dip angle and the fault throw according to the regional adjustment coefficient of the slopes in the loess area, and determining the third influence degree of the fault factor on the slope stability in the loess area according to the relationship between the adjusted fault dip angle and the fault throw and the threshold.

[0068] Step S123, determining the fourth influence degree of the earthquake factor on the slope stability in the loess region according to the adjusted minimum earthquake magnitude that can induce slope collapse and sliding during a natural earthquake, the relationship between the minimum site intensity that can induce slope collapse and sliding during a natural earthquake and the threshold.

[0069] In some of the embodiments, in step S121, a 50 cm resolution satellite remote sensing image can be obtained by using UAV remote sensing technology, and the cracking of the land can be determined in the remote sensing image according to the characteristics of the associated ground fissures formed by mining activities in DOM and infrared images. The associated ground fissures in the target area can be remotely interpreted and identified by direct signs with obvious crack linear texture and contrast with the surrounding colors, and indirect signs of traces left by the filling of cracks due to natural or artificial factors. Among them, when the width of the associated ground fissures is less than 5 cm, it is preliminarily judged that the ground fissures have the least impact on the stability of the slope (for example, the fourth level of impact); when the width of the associated ground fissures is between 5 and 8 cm, it is preliminarily judged that the ground fissures have a small impact on the stability of the slope (for example, the third level of impact); when the width of the associated ground fissures is between 8 and 10 cm, it is preliminarily judged that the ground fissures have a large impact on the stability of the slope (for example, the second level of impact); when the width of the associated ground fissures is greater than 10 cm, it is preliminarily judged that the ground fissures have the greatest impact on the stability of the slope (for example, the first level of impact). That is, the preset interval to which the ground fissure width belongs may include: <5 cm, or within 5 to 8 cm, or within 8 to 10 cm, or >10 cm.

[0070] In some of the embodiments, in step S122, the identification of the fault position can be combined with remote sensing images and digital elevation model DEM data for comprehensive analysis. The use of digital elevation model DEM data can quantitatively reflect the elevation information of the surface and can simulate any illumination angle when establishing a shadow map, which can make up for the deficiency that the acquisition of remote sensing images is based on certain optical parameters and can only quantitatively obtain two-dimensional information. It is also possible to combine high-resolution remote sensing images with low-resolution digital elevation model DEM data to perform three-dimensional terrain superposition, so as to more accurately extract structural information from multiple angles. Usually, after determining the position of the fault, the inclination and undulation of the fault can be judged by remote sensing data. Exemplarily, when the adjusted fault inclination is greater than 20° and the drop is greater than 10m, it is considered that the fault has a significant impact on the study area, that is, the first level of impact. That is, the threshold of the fault inclination is 20°, and the threshold of the fault drop is 10m. After the adjusted fault inclination and fault drop are both greater than the threshold, it is determined that the fault factor has a significant impact on the slope stability, that is, the first level of impact.

[0071] In some embodiments, in step S123, the minimum earthquake magnitude M that can induce slope collapse and sliding during a natural earthquake is determined. o , the minimum site intensity I that can induce slope collapse during a natural earthquake so The influence of earthquake on the stability of slopes in the target area can be judged. For example, when the magnitude Mo in the study area is greater than 5, it is considered that if an earthquake occurs, the impact on the stability of the slopes in the study area will be greater; when the site intensity Iso in the study area is greater than VI, it is considered that if an earthquake occurs, the impact on the stability of the slopes in the study area will be greater. That is, the threshold value of the minimum earthquake magnitude that can induce slope collapse and sliding during a natural earthquake can be 5, and the threshold value of the minimum site intensity that can induce slope collapse and sliding during a natural earthquake can be VI. When the adjusted minimum earthquake magnitude that can induce slope collapse and sliding during a natural earthquake is greater than the corresponding threshold, and the minimum site intensity that can induce slope collapse and sliding is greater than the corresponding threshold, it is determined that the earthquake factor has a significant impact on the stability of the slope, that is, the first level of influence.

[0072] In some of the embodiments, the method of obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the Loess region, and judging whether the meteorological and hydrological condition factor has an adverse effect on the slope stability according to the adjusted value includes: obtaining a water system model in the digital elevation model DEM data, inputting rainfall monitoring data of a meteorological station in the Loess region, and obtaining regional average rainfall intensity data; adjusting the rainfall intensity data according to the regional adjustment coefficient of the slope in the Loess region, and determining the fifth degree of influence of the rainfall factor on the slope stability according to the relationship between the adjusted rainfall intensity data and the preset intensity data; the fifth degree of influence includes the first level of influence.

[0073] In some of the embodiments, a digital elevation model with a resolution of 30m in a digital elevation model DEM database can be used, through the spatial analysis module of the geographic information system processing software, and by using spatial interpolation simulation, the water system model obtained from the DEM data is combined with the site data of the annual average precipitation in the loess region to simulate and predict the precipitation distribution and obtain the rainfall intensity in the study area. When the rainfall in 24 hours is ≥50mm, that is, the rainfall intensity is expressed as heavy rain or above, the rainfall is likely to have an adverse effect on the slopes in the loess region in the study area, that is, the first level of influence. That is, the preset intensity data is 50mm. According to the relationship between the adjusted rainfall intensity data and the preset intensity data, determining whether the rainfall factor has an adverse effect on the slope stability may include, in response to determining that the adjusted rainfall intensity data is greater than the preset intensity data, determining that the rainfall factor has an adverse effect on the slope stability (that is, the first level of influence).

[0074] In some embodiments, obtaining the values ​​of each factor in the structural factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence degree of the slope degree data in the structural factor on the slope stability and whether the slope aspect data in the structural factor has an adverse effect on the slope stability according to the adjusted value may include:

[0075] Step S141, obtaining the NDVI value corresponding to the slope aspect data in the digital elevation model DEM data; adjusting the NDVI value according to the regional adjustment coefficient of the slope in the loess region, and determining the sixth degree of influence of the slope aspect on the stability of the slope in the loess region according to whether the adjusted NDVI value is within a preset interval. The sixth degree of influence includes the first level of influence or the second level of influence.

[0076] Step S142, obtaining slope height and slope gradient data in the digital elevation model DEM data; adjusting the slope height and slope gradient data according to the regional adjustment coefficient of the slope in the loess region, and determining the seventh influence degree of the slope height on the slope stability in the loess region and the eighth influence degree of the slope gradient on the slope stability in the loess region according to the preset interval to which the adjusted slope height belongs; the seventh influence degree includes the first level influence degree, the second level influence degree, the third level influence degree or the fourth level influence degree; the eighth influence degree includes the first level influence degree, the second level influence degree or the third level influence degree.

[0077] In some of the embodiments, in step S141, terrain factors can be extracted from DEM data by using the corresponding analysis tools of the geographic information system processing software. Specifically, by inputting DEM data, since the north direction of the slope is specified as 0° in the geographic information system processing software, it is calculated in a clockwise direction with a value range of 0° to 360°. The reclassification tool is used to divide the slope into 16 types of slopes with an interval of 22.5°. Afterwards, by merging the classifications, the slopes of the study area are divided into 8 types of slopes: east, southeast, south, southwest, west, northwest, north, and northeast. At the same time, a DEM-NDVI scatter distribution map is constructed using NDVI and DEM to determine the vegetation coverage range of each slope direction. The coverage degree of the ecological vegetation distribution area of ​​each slope direction is judged according to the NDVI value, and the soil and water loss of the slope in the loess area is judged. Among them, the NDVI value range is -1 to 1. When the NDVI is a negative value, it means that the ground is covered with clouds, water, snow, etc., and the data needs to be updated for further analysis; when the NDVI is 0, it means that there are rocks or bare soil, etc., and the less vegetation has a poor slope stability, which is the first level of influence; when the NDVI is a positive value, it means that there is vegetation coverage, and it increases with the increase of coverage, and the slope stability is better, which is the second level of influence. That is, the preset value corresponding to the NDVI value corresponding to the adjusted slope aspect data can be 0. The sixth degree of influence of the slope aspect on the slope stability in the loess region according to whether the adjusted NDVI value is the preset value may include: in response to determining that the adjusted NDVI value is the preset value, determining that the slope aspect has an adverse effect on the slope stability (that is, the first level of influence).

[0078] In some embodiments, in step S142, the DEM data of the study area can be loaded, the required data range can be clipped, and the raster data can be obtained by using the extract by mask command, and the attribute symbol system-classified can be selected. The category and classification can be selected according to the actual elevation situation. The natural break point method can be selected to be divided into 10 categories to obtain the elevation analysis map of the study area; through the slope command, the raster data is input to obtain the slope angle analysis map. Then, based on the elevation and slope analysis maps, the slope height and slope are obtained, and the influence of the slope height on the stability of the slope in the loess region is judged. According to the influence of the slope gradient on the growth of vegetation, the soil and water loss of the slope in the loess region is judged.

[0079] In some of the embodiments, illustratively, when the slope height is between 0 and 10 m, the impact on slope stability is minimal; when the slope height is between 10 and 15 m, the impact on slope stability is small; when the slope height is between 15 and 30 m, the impact on slope stability is large; when the slope height is greater than 30 m, the impact on slope stability is the greatest. When the slope gradient is between 0% and 27.1%, the impact on slope stability is small; when the slope gradient is between 27.1% and 70%, it has an impact on slope stability; when the slope gradient is between 70% and 100%, the impact on slope stability is large. In the seventh degree of influence, the first level of influence may be the greatest impact, and the corresponding slope height may be greater than 30 m. The second level of influence may be a greater impact, and the corresponding slope height may be within 15 to 30 m. The third level of influence may be a smaller impact, and the corresponding slope height may be within 10 to 15 m. The fourth level of impact can be the least impact, and the corresponding slope height can be within the interval of 0 to 10 m. In the eighth level of impact, the first level of impact can be the greater impact, and the corresponding slope gradient can be within the interval of 70% to 100%, the second level of impact can be the impact, and the corresponding slope gradient can be within the interval of 0% to 27.1%, and the third level of impact can be the less impact, and the corresponding slope gradient can be within the interval of 27.1% to 70%.

[0080] In some embodiments, in step S200, determining the second hidden danger area in the first hidden danger area according to the location factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining process and the slope type may include:

[0081] Step S210, according to the position of the mined void area adjacent to the slope position of the loess area in the upper and lower comparison map of the mining area, according to L1=cosα1·h1+cotβ1·(H1-h1), determine the influence range of the mined void area; wherein L1 represents the collapse influence range; α1 represents the loose layer movement angle; H1 represents the loose layer thickness; β1 represents the bedrock movement angle; h1 represents the burial depth.

[0082] Step S220, according to the planned unmined goaf position adjacent to the loess area slope position in the mine well up-down comparison map, according to L2=cosα2·h2+cotβ2·(H2-h2), determine the influence range of the unmined goaf; where L2 represents the collapse influence range; α2 represents the loose layer movement angle; H2 represents the loose layer thickness; β2 represents the bedrock movement angle; h2 represents the burial depth;

[0083] Step S230, based on the degree of influence of the interval of mining depth and thickness ratio in the coal mining subsidence area on the surface deformation, the degree of influence of the coal mining technology of the slope on the surface deformation and the corresponding degree of influence of the slope type, determine the hidden danger area overlying the mined goaf area and the unmined goaf area to obtain the second hidden danger area.

[0084] In some embodiments, in step S220, for the position of the unmined goaf area, if there is an overlap with the position of the mined goaf area, the range of the overlapping area can be determined, and the overlapping area is the key hidden danger area in the second hidden danger area. That is, the method may also include: in response to determining that the position of the unmined goaf area overlaps with the position of the mined goaf area, determining that the overlapping area is a third hidden danger area, only performing stability prediction in step S400 on the slope of the coal mining subsidence area in the third hidden danger area, and treating the slope of the coal mining subsidence area with unstable prediction results.

[0085] In some embodiments, in step S230, the hidden danger area overlying the mined goaf position and the unmined goaf position is determined according to the influence degree of the interval of the mining depth and thickness ratio in the coal mining subsidence area on the surface deformation, the influence degree of the coal mining process of the slope on the surface deformation and the influence degree corresponding to the slope type, and the second hidden danger area is obtained, including:

[0086] Step S231, obtain the mining depth and thickness ratio of the coal mining subsidence area, and determine the ninth degree of influence of the interval to which the mining depth and thickness ratio of the coal mining subsidence area belongs on the surface deformation, wherein the ninth degree of influence includes the first level of influence, the second level of influence, the third level of influence, the fourth level of influence or the fifth level of influence.

[0087] Step S232, obtaining the coal mining technology of the slope, and determining the tenth influence degree of the coal mining technology of the slope on the surface deformation, wherein the tenth influence degree includes the first level influence degree and the second level influence degree.

[0088] Step S233, obtaining the slope type, including a rock slope or a soil slope; determining the eleventh degree of influence of the rock slope type on the slope stability, including the first level of influence, the second level of influence, the third level of influence or the fourth level of influence; or obtaining the foundation soil type of the soil slope, determining the twelfth degree of influence of the foundation soil type on the slope stability, including the first level of influence.

[0089] Step S234, in response to determining that the ninth impact degree is the first level of impact degree or the second level of impact degree, the tenth impact degree is the first level of impact degree, the eleventh impact degree or the twelfth impact degree is the first level of impact degree, determining that the area where the slope is located is a second hidden danger area.

[0090] In some embodiments, in step S231, the size of the mining depth and thickness ratio will affect the ground deformation of the goaf. Specifically, the location of the goaf can be identified by the upper and lower comparison diagrams of the well, and the degree of surface deformation can be further determined according to the mining depth and thickness ratio of the mined coal seam. When the mining depth and thickness ratio is less than 30, the surface is mostly violently deformed, and the impact is the greatest; when the mining depth and thickness ratio is between 30 and 60, the surface deformation is relatively weakened, but the impact is greater; when the mining depth and thickness ratio is between 60 and 90, the surface deformation gradually weakens, and the impact is small; when the mining depth and thickness ratio is between 90 and 120, the surface deformation is further weakened, and the impact is very small; when the mining depth and thickness ratio is greater than 120, the surface generally has no obvious signs of movement and deformation, and the impact is minimal. That is, among the influence levels corresponding to the intervals of the mining depth and thickness ratio in the coal mining subsidence area, the first level of influence level can be the greatest impact, and the corresponding interval can be the mining depth and thickness ratio less than 30. The second level of influence level can be a large impact, and the corresponding interval can be the mining depth and thickness ratio within 30 to 60. The third level of impact may be a small impact, and the corresponding interval may be a mining depth-thickness ratio of 60 to 90. The fourth level of impact may be a very small impact, and the corresponding interval may be a mining depth-thickness ratio of 90 to 120. The fifth level of impact may be a minimum impact, and the corresponding interval may be a mining depth-thickness ratio of more than 120.

[0091] In some of the embodiments, in step S232, in underground mining, the surface deformation value generated by the long-arm coal mining method is relatively large, and the surface deformation values ​​generated by the room-and-pillar coal mining method, the strip coal mining method, and the backfill coal mining method are relatively small. At the same time, the impact of repeated mining in the goaf on the surface deformation is much greater than the initial mining. Combined with the upper and lower comparison diagrams of the well and the layout of the excavation and mining project, the application of the long-arm coal mining method and the scope of the repeated mining area are focused on identifying and judging. That is, among the influence degrees of the coal mining process on the surface deformation, the first level of influence degree can be that the coal mining process has a greater influence on the surface deformation value, and the corresponding coal mining process can be the long-arm coal mining method or repeated mining in the goaf. The second level of influence degree can be that the coal mining process has a smaller influence on the surface deformation value, and the corresponding coal mining process can be the room-and-pillar coal mining method, the strip coal mining method, or the backfill coal mining method.

[0092] In some embodiments, in step S233, the slope type is mainly divided into rock slope and soil slope. Among them, in the geological structure classification of rock slope, the slope stability conditions are from high to low: integral block structure slope, layered structure slope, fragmented structure slope, and dispersed structure slope; soil slope usually considers the impact of engineering activities on hydrogeological conditions within the slope influence range. In addition, if the foundation material is a poor foundation soil (soft clay, backfill, miscellaneous fill, saturated loose sand, collapsible loess, expansive soil, organic soil and peat soil, mountain foundation soil, moraine soil), it will affect the slope stability of the mining area. That is, among the stability influence degrees corresponding to the rock slope types, the integral block structure slope corresponds to the first level of influence (e.g., the greatest influence), the layered structure slope corresponds to the second level of influence (e.g., the greater influence), the fragmented structure slope corresponds to the third level of influence (e.g., the less influence), and the dispersed structure slope corresponds to the fourth level of influence (e.g., the least influence). The obtaining of the foundation soil type of the earth slope and determining whether the foundation soil type has an adverse effect on the slope stability includes determining that the foundation soil in the area has an adverse effect on the slope stability (i.e., the first level of influence) when the foundation soil type of the earth slope is poor foundation soil (soft clay, fill soil, miscellaneous fill soil, saturated loose sand, collapsible loess, expansive soil, soil containing organic matter and peat, mountain foundation soil or tillage-containing soil).

[0093] In some of the embodiments, in step S300, the training of the random forest slope stability prediction model may include:

[0094] Acquire a training data set; the training data set includes slope cohesion, slope internal friction angle, slope height, slope angle, average bulk density of slope rock and soil materials, and slope pore pressure ratio;

[0095] The pre-built random forest slope stability prediction model is trained by using the training data set to obtain a preliminarily trained random forest slope stability prediction model.

[0096] The test data set is input into the support vector machine model to obtain a first prediction result; the test data set is input into the preliminarily trained random forest slope stability prediction model to obtain a second prediction result. Generally, the support vector machine model can be a pre-trained model.

[0097] The generalization error between the first prediction result and the second prediction result is calculated. Generally, the generalization error may include bias and variance.

[0098] In response to determining that the generalization error is less than the preset error, the training of the random forest slope stability prediction model is terminated. That is, the initially trained random forest slope stability prediction model is determined to be the final random forest slope stability prediction model. Usually, the preset error can be set according to the requirements of the mining area. Or in response to determining that the generalization error is greater than or equal to the preset error, return to the step of training the pre-constructed random forest slope stability prediction model through the training data set.

[0099] In practical applications, the training data set can be 80 groups of different slope stability examples with clear conclusions and containing the selected influencing factors, which can be used as training samples for the random forest prediction model. 20 groups of different slope stability examples with slope instability such as collapse and containing the selected influencing factors can be used as support vector machine validation group samples, and the output result is set as 0 for the slope to have instability risk, and 1 for slope stability. Further, 80 groups of measured data sets of slopes are input, the number of trees of the random forest model is set to 10 and the number of variables is set to 3, and the model is trained to form the corresponding random forest prediction model. At the same time, for the 20 groups of validation group sample test data with slope instability such as collapse, the support vector machine algorithm is used for simulation verification and comparison. For the large sample data set of 80 groups of different slope stability examples with clear conclusions and containing the selected influencing factors and 20 groups of different slope stability examples with slope instability such as collapse and containing the selected influencing factors, the 100 groups of data can also be randomly divided into 10 subsets of the same number through the ten-fold cross validation method, and one group is randomly selected as the test data set, and the others are used as the training data set. The random forest algorithm and support vector machine algorithm were used for model training respectively, and the generalization error of each algorithm model under the selected 100 groups of samples was calculated.

[0100] In some of the embodiments, the method may also include: carrying out different prevention and control measures on the first hidden danger area, the second hidden danger area and the slopes of the coal mining subsidence area whose stability prediction result is unstable; wherein, the slopes of the first hidden danger area are periodically monitored and the slopes of the second hidden danger area are multi-dimensionally monitored; and the slopes of the coal mining subsidence area whose stability prediction result is unstable are treated.

[0101] It can be understood that after regional-level slope identification, InSAR data can be used to regularly observe unstable slopes in the first hidden danger area to achieve early warning reminders; after mining-level slope evaluation, the unstable slopes in the mining area of ​​the second hidden danger area can be predicted and prevented in real time through multi-dimensional slope radar; after high-risk slope prediction, based on a variety of slope protection measures, specific high-risk slopes in multiple coal mining subsidence areas can be targeted for slope protection and management. In this way, based on the evaluation results of the slope stability of the coal mining subsidence area, corresponding prevention and control can be carried out for slopes at different evaluation stages (i.e., slopes in the first hidden danger area, slopes in the second hidden danger area, and slopes in the third hidden danger area).

[0102] In some embodiments, the periodic monitoring of the first hidden danger area includes: quarterly detection of the slopes of the first hidden danger area, and in response to determining that the impact of the first hidden danger area has increased between years, generating regional warning information of the increased impact of the first hidden danger area between quarters; the warning information is used to indicate that the impact of the first hidden danger area has deepened. Specifically, for the identification results of regional-level slopes, space remote sensing technology can be used to achieve large-scale periodic monitoring in the region. InSAR data of the study area at intervals of one quarter are selected for regional-level identification. Based on the identification result range of factors with greater / poorer impacts on the slopes and more, and three or more factors with less impacts on the slopes, the range of hidden danger slopes in the study area between quarters is regularly observed to provide reminders and warnings for areas with increased hidden danger ranges between years.

[0103] In some embodiments, the multi-dimensional monitoring of the second hidden danger area includes: monthly detection of the slope of the second hidden danger area, and deployment of slope deformation measurement devices and crack sensors for real-time detection, and generating alarm information in response to determining that the slope deformation data and the displacement data of the associated ground fissures exceed the corresponding warning threshold; the alarm information is used to indicate that the slope is deformed and unstable, and an on-site survey is required. Specifically, a multi-dimensional slope monitoring system can be established to conduct regular dynamic observations on the range of major impact hazards in the mining area-level slope evaluation results. On the one hand, the slope monitoring radar using real aperture radar technology is used to perform step-by-step line-by-line scanning of the slope hidden danger area in the mining area between each month to obtain a real three-dimensional image; on the other hand, deformation measurement devices such as geological disaster monitoring instruments and crack sensors are arranged in the slope hidden danger area, and the alarm program is activated when the monitored deformation and associated ground fissure data exceed the set warning threshold, so as to achieve the prediction and prevention of hidden danger slopes in the mining area.

[0104] In some of the embodiments, the treatment of the loess slopes in the coal mining subsidence area with unstable stability prediction results includes: for the loess slopes in the coal mining subsidence area with unstable stability prediction results, corresponding treatment strategies are generated according to the different geological environment backgrounds and types of slope rock and soil properties in which the slopes are located. Specifically, high and steep slopes can be treated by slope support engineering and dangerous rock removal methods. When the existence of fissures affects the slopes in the loess area, they can be treated by grouting reinforcement and sealing technology. For the slopes in the loess area with high water permeability, they can be treated by drainage technology. For the slopes in the loess area with easily weathered surfaces, the slope surface can be reinforced by means such as ecological restoration. Based on different geological environment background factors, corresponding treatment technologies can be combined to establish a specific high-risk loess area slope treatment system. For example, for high and steep loess slopes that are seriously affected by groundwater, the loess slopes can be treated by "slope drainage + slope cutting and load reduction".

[0105] In some of the embodiments, after the slopes in the coal mining subsidence area whose stability prediction results are unstable are treated, the area can also be monitored emphatically in the subsequent mining area-level slope prediction and prevention process.

[0106] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0107] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting the stability of the overlying slope in the coal mining subsidence area in the loess region described in any of the above embodiments is implemented.

[0109] Figure 3A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0110] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0111] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0112] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0113] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0114] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040).

[0115] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0116] The electronic device of the above embodiment is used to implement the corresponding method for predicting the stability of the overlying slope of the coal mining subsidence area in the loess region in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0117] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for predicting the stability of the overlying slope in the coal mining subsidence area in the loess region as described in any of the above embodiments.

[0118] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0119] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the method for predicting the stability of the overlying slope in the coal mining subsidence area in the loess region as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0120] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0121] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply / ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0122] Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0123] The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims

1. A method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions, characterized in that: include: According to the environmental factors of the slopes in the loess region, the structural factors of the slopes in the loess region and the regional adjustment coefficients of the slopes in the loess region, the first hidden danger area in the target area is determined; The environmental factors include geographical factors, geological environmental background factors and meteorological and hydrological conditions; the target area is a loess area, including multiple coal mining areas and coal mining subsidence areas using underground mining, and has a preset area range; the first hidden danger area includes at least one coal mining area and coal mining subsidence area using underground mining; Determine the second hidden danger area in the first hidden danger area according to the location factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining process and the slope type; the second hidden danger area includes at least one slope with instability hidden danger in the coal mining subsidence area; the location factor includes the range of the mined goaf area and the range of the unmined goaf area; Obtain quantitative values ​​of stability influencing factors of the slope with instability hazards in the at least one coal mining subsidence area, input them into the random forest slope stability prediction model, and obtain the stability prediction result of the slope in the at least one coal mining subsidence area; the stability prediction result includes stable or unstable; the quantitative values ​​of the stability influencing factors include slope cohesion, slope internal friction angle, slope height, slope angle, average bulk density of slope rock and soil materials, and slope pore pressure ratio.

2. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 1 is characterized in that: Also includes: Different prevention and control measures are taken for the first hidden danger area, the second hidden danger area and the slopes in the coal mining subsidence area with unstable stability prediction results; among which, the slopes in the first hidden danger area are periodically monitored and the slopes in the second hidden danger area are monitored in multiple dimensions; the slopes in the coal mining subsidence area with unstable stability prediction results are treated.

3. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 2 is characterized in that: The periodic monitoring of the first hidden danger area includes: performing quarterly detection on the slope of the first hidden danger area, and generating regional warning information of the increased impact of the first hidden danger area between quarters in response to determining that the impact of the first hidden danger area increases between quarters; the warning information is used to indicate that the impact of the first hidden danger area is deepening; The multi-dimensional monitoring of the second hidden danger area includes: monthly detection of the slope of the second hidden danger area, and deployment of slope deformation measurement devices and crack sensors for real-time detection, and generating alarm information in response to determining that the slope displacement data and the associated ground crack displacement data both exceed corresponding warning thresholds; the alarm information is used to indicate that there is a risk of deformation and instability in the slope, and an on-site survey is required; The treatment of the slope in the coal mining subsidence area whose stability prediction result is unstable includes: for the slope in the coal mining subsidence area whose stability prediction result is unstable, a corresponding treatment strategy is generated according to the geological environment background of the slope and the properties of the slope rock and soil.

4. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 1 is characterized in that: The geographical factors include topographic factors; the geological environment background factors include ground fissure factors, fault factors and earthquake factors; the meteorological and hydrological conditions factors include rainfall factors; the structural factors include slope height factors, slope gradient factors and slope aspect factors.

5. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 4 is characterized in that: Determining the first hidden danger area in the target area according to the environmental factors of the loess region slope, the structural factors of the loess region slope and the regional adjustment coefficient of the loess region slope includes: Obtaining the value of the geographical factor, adjusting the value of the geographical factor according to the regional adjustment coefficient, and judging the influence of the geographical factor on the slope stability in the loess region according to the adjusted value; Obtaining the value of each factor in the geological environment background factor, adjusting the value of each factor in the geological environment background factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of each factor in the geological environment background factor on the stability of the slope in the loess region according to the adjusted value; Obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the meteorological and hydrological condition factor on the stability of the slope in the loess region according to the adjusted value; Obtaining the values ​​of each factor in the structural factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the slope height and slope gradient data in the structural factor on the slope stability, and the influence of the slope aspect data in the structural factor on the slope stability in the loess region according to the adjusted value; In response to determining that the influence of the geographical factors on the slope stability is the first level of influence or the second level of influence, the influence of each factor in the geological environment background factors on the slope stability is the first level of influence, the influence of the meteorological and hydrological conditions on the slope stability is the first level of influence, the influence of the slope height and slope gradient data in the structural factors on the slope stability is the first level of influence, and the influence of the slope aspect data in the structural factors on the slope stability is the first level of influence; determine that the area where the slope is located is the first hidden danger area.

6. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 5 is characterized in that: The obtaining of the value of the geographical factor, adjusting the value of the geographical factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the geographical factor on the slope stability according to the adjusted value comprises: obtaining the terrain relief in the digital elevation model DEM data; adjusting the terrain relief according to the regional adjustment coefficient of the slope in the loess region, and determining the first influence of the terrain and geomorphic factors on the stability of the slope in the loess region according to the preset interval to which the adjusted terrain relief belongs; the first influence comprises a first level of influence, a second level of influence, a third level of influence, a fourth level of influence or a fifth level of influence; The step of obtaining the value of each factor in the geological environment background factors, adjusting the value of each factor in the geological environment background factors according to the regional adjustment coefficient of the slope in the loess region, and judging the influence degree of each factor in the geological environment background factors on the stability of the slope in the loess region according to the adjusted value comprises: obtaining the associated ground fissures in the satellite remote sensing image data; adjusting the associated ground fissures data according to the regional adjustment coefficient of the slope in the loess region, and determining the second influence degree of the ground fissure factors on the stability of the slope in the loess region according to the preset interval to which the width of the adjusted associated ground fissures belongs; the second influence degree comprises the first level influence degree, the second level influence degree, the third level influence degree or the fourth level influence degree; Obtaining the fault position in the digital elevation model DEM data and the satellite remote sensing image data; obtaining the fault dip and fault drop in the satellite remote sensing image data; adjusting the fault dip and fault drop according to the regional adjustment coefficient of the slope in the loess region, and determining the third influence degree of the fault factor on the stability of the slope in the loess region according to the relationship between the adjusted fault dip and fault drop and the threshold; the third influence degree includes the influence degree of the first level; Determine the fourth degree of influence of earthquake factors on slope stability in the loess region according to the adjusted minimum earthquake magnitude that can induce slope collapse and sliding during a natural earthquake, and the relationship between the minimum site intensity that can induce slope collapse and sliding during a natural earthquake and the threshold value; the fourth degree of influence includes the first level of influence; The step of obtaining the value of the meteorological and hydrological condition factor, adjusting the value of the meteorological and hydrological condition factor according to the regional adjustment coefficient of the slope in the loess region, and judging the influence degree of the meteorological and hydrological condition factor on the stability of the slope in the loess region according to the adjusted value comprises: obtaining a water system model in the digital elevation model DEM data, inputting the rainfall monitoring data of the meteorological station in the loess region, and obtaining the regional average rainfall intensity data; adjusting the rainfall intensity data according to the regional adjustment coefficient of the slope in the loess region, and determining the fifth influence degree of the rainfall factor on the stability of the slope in the loess region according to the relationship between the adjusted rainfall intensity data and the preset intensity data; the fifth influence degree includes the influence degree of the first level; The method of obtaining the values ​​of each factor in the structural factors, adjusting the values ​​of the meteorological and hydrological condition factors according to the regional adjustment coefficient of the slope in the loess region, and judging the influence of the slope height and slope gradient in the structural factors on the stability of the slope in the loess region according to the adjusted values, and the influence of the slope aspect data in the structural factors on the stability of the slope in the loess region include: Obtaining the NDVI value corresponding to the slope aspect data in the digital elevation model DEM data; adjusting the NDVI value according to the regional adjustment coefficient of the slope in the loess region, and determining the sixth degree of influence of the slope aspect on the stability of the slope in the loess region according to whether the adjusted NDVI value is a preset value; the sixth degree of influence includes the first level of influence or the second level of influence; Obtain slope height and slope gradient data in digital elevation model (DEM) data; adjust the slope height and slope gradient data according to the regional adjustment coefficient of the slope in the loess region, and determine the seventh influence degree of the slope height on the stability of the slope in the loess region and the eighth influence degree of the slope gradient on the slope stability according to the preset interval to which the adjusted slope height belongs; the seventh influence degree includes the first level influence degree, the second level influence degree, the third level influence degree or the fourth level influence degree; the eighth influence degree includes the first level influence degree, the second level influence degree or the third level influence degree.

7. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 5, characterized in that: Determining the second hidden danger area in the first hidden danger area according to the position factor of the slope in the loess area, the mining depth and thickness ratio of the coal mining subsidence area, the coal mining technology and the slope type includes: According to the location of the mined goaf area adjacent to the slope position in the upper and lower comparison diagram of the mine well, the influence range of the mined goaf area is determined according to L1=cosα1·h1+cotβ1·(H1-h1); where L1 represents the collapse influence range; α1 represents the loose layer movement angle; H1 represents the loose layer thickness; β1 represents the bedrock movement angle; h1 represents the burial depth; According to the planned unmined goaf position close to the slope position in the upper and lower comparison map of the mine well, the influence range of the unmined goaf is determined according to L2=cosα2·h2+cotβ2·(H2-h2); where L2 represents the collapse influence range; α2 represents the loose layer movement angle; H2 represents the loose layer thickness; β2 represents the bedrock movement angle; h2 represents the burial depth; According to the degree of influence of the mining depth and thickness ratio range in the coal mining subsidence area on the surface deformation, the degree of influence of the slope mining technology on the surface deformation and the corresponding degree of influence of the slope type, the hidden danger areas overlying the mined goaf areas and the unmined goaf areas are judged to obtain the second hidden danger areas.

8. The method for predicting the stability of overlying slopes in coal mining subsidence areas in loess regions according to claim 7, characterized in that: According to the influence degree of the interval of the mining depth and thickness ratio in the coal mining subsidence area on the surface deformation, the influence degree of the coal mining technology of the slope on the surface deformation and the influence degree corresponding to the slope type, the overlying hidden danger area of ​​the mined goaf area and the unmined goaf area is judged, and the second hidden danger area includes: Obtaining the mining depth and thickness ratio of the coal mining subsidence area, and determining the ninth degree of influence of the interval to which the mining depth and thickness ratio of the coal mining subsidence area belongs on the surface deformation; the ninth degree of influence includes the first level of influence, the second level of influence, the third level of influence, the fourth level of influence or the fifth level of influence; Obtaining the coal mining technology of the slope, and determining the tenth degree of influence of the coal mining technology of the slope on the surface deformation; the tenth degree of influence includes the first level of influence or the second level of influence; Obtain the type of slope, including rock slope or soil slope; determine the eleventh degree of influence of the type of rock slope on slope stability, including the first degree of influence, the second degree of influence, the third degree of influence or the fourth degree of influence; or obtain the type of foundation soil of the soil slope, determine the twelfth degree of influence of the foundation soil type on slope stability, including the first degree of influence; In response to determining that the ninth impact degree is the first level impact degree or the second level impact degree, the tenth impact degree is the first level impact degree, and the eleventh impact degree or the twelfth impact degree is the first level impact degree, the area where the slope is located is determined to be a second hidden danger area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 8 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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